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$Schillings, Claudia, A General Framework for Machine Learning based Optimization Under Uncertainty$

03. February 2022

Schillings, Claudia, A General Framework for Machine Learning based Optimization Under Uncertainty

Online Talk within the Seminar Series on Scientific Computing.

$Prof. R. Zimmermann, How to interpolate low-rank matrix decompositions? Computing the Riemannian normal coordinates in the space of orthogonal frames$

27. January 2022

Prof. R. Zimmermann, How to interpolate low-rank matrix decompositions? Computing the Riemannian normal coordinates in the space of orthogonal frames

Online Talk within the Seminar Series on Scientific Computing.

18. January 2022

Talk by Dr. Panpan Ren on "Exponential Convergence in Entropy and Wasserstein Distance for (Time Periodic) Distribution Dependent SDEs"

Weakly seminar of the research group "Functional Analysis and Stochastic Analysis" on Tuesday, January 18, 2022, at 16:00 in 48-580.

Motivbild Disputation: Doktorhut; Foto: SOK

14. January 2022

Dietz, Tobias: Combinatorial Optimization in Digital Communications

Disputation, 14. Januar 2022, um 14:00 Uhr

$Dr. Domenico Quagliarella, Intrusive and non-intrusive approximation techniques for efficient robust aerodynamic shape design using risk functions$

13. January 2022

Dr. Domenico Quagliarella, Intrusive and non-intrusive approximation techniques for efficient robust aerodynamic shape design using risk functions

Online Talk within the Seminar Series on Scientific Computing.

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Schillings, Claudia, A General Framework for Machine Learning based Optimization Under Uncertainty

Prof. R. Zimmermann, How to interpolate low-rank matrix decompositions? Computing the Riemannian normal coordinates in the space of orthogonal frames

Talk by Dr. Panpan Ren on "Exponential Convergence in Entropy and Wasserstein Distance for (Time Periodic) Distribution Dependent SDEs"

Dietz, Tobias: Combinatorial Optimization in Digital Communications

Dr. Domenico Quagliarella, Intrusive and non-intrusive approximation techniques for efficient robust aerodynamic shape design using risk functions

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